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Accurate retrieval of ocean surface wind speed (OSWS) has a vital impact on maritime transportation planning and extreme weather forecast. Current models leveraging deep-learning (DL) techniques have demonstrated considerable potential for satellite remote-sensing wind retrieval. However, these models tend to focus on synchronizing the retrieved wind speeds with the label, neglecting the inherent absolute error (AE) embedded within the label and thus resulting in retrieval errors. To mitigate the disruptive impact of AE on retrieval accuracy, we introduce a novel network called TCNet, which retrieves observations of cyclone global navigation satellite system (CYGNSS) as OSWS. The network constructs an AE module (AEM), guided by triple collocation (TC) method for improved accuracy in real-time wind retrieval by calculating the AE as loss value. These calculations guide the network training process, thereby enhancing retrieval accuracy. Meanwhile, the wind speed dataset imbalance and inherent averaging characteristics of networks frequently result in wind speed uncertaintines in extremes. Notably, this occurs as a gross underestimation of high-speed winds. Therefore, TCNet incorporates an adaptive penalty module (APM) to solve this problem. By assigning higher penalty factors to high-speed winds, the sensitivity of network to its retrieval is improved. Experimentally, the APM in TCNet exhibited a remarkable reduction of AE in high-speed wind retrieval and mitigates the understating of high-speed scenarios while maintaining an overall error that is not significantly increased. Importantly, TCNet demonstrated notable resistance to noise and portrayed excellent generalizability, providing fresh insights into weather forecasting, climate research, and other marine applications.